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The main purpose of the course is to give students the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning with big data tools such as HDInsight and R Services.

Microsoft On DemandMicrosoft Official Courses On-Demand (MOC On-Demand) blend video, text, hands-on labs and knowledge checks to help you build your Microsoft technology skills on your own schedule, at your own pace and in your own place. No need to spend time and money travelling to a classroom location or adhering to classroom hours. With a computer and an Internet connection, your Microsoft Official Courses On-Demand come to you, any time.

Microsoft On Demand covers the same objectives as the classroom course by the same name

After you activate your course, you have 3 months to complete it. You can work on your course at any time throughout the 3-month period after you activate the course.

Audience ProfileThe primary audience for this course is people who wish to analyze and present data by using Azure Machine Learning.The secondary audience is IT professionals, Developers , and information workers who need to support solutions based on Azure machine learning.

PrerequisitesIn addition to their professional experience, students who attend this course should have:

Programming experience using R, and familiarity with common R packages

Knowledge of common statistical methods and data analysis best practices.

Basic knowledge of the Microsoft Windows operating system and its core functionality.

Working knowledge of relational databases.

Course ObjectivesAfter completing this course, students will be able to:

Explain machine learning, and how algorithms and languages are used

Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio

Upload and explore various types of data to Azure Machine Learning

Explore and use techniques to prepare datasets ready for use with Azure Machine Learning

Explore and use feature engineering and selection techniques on datasets that are to be used with Azure Machine Learning

Explore and use regression algorithms and neural networks with Azure Machine Learning

Explore and use classification and clustering algorithms with Azure Machine Learning

Use R and Python with Azure Machine Learning, and choose when to use a particular language

Explore and use hyperparameters and multiple algorithms and models, and be able to score and evaluate models

Explore how to provide end-users with Azure Machine Learning services, and how to share data generated from Azure Machine Learning models

Explore and use the Cognitive Services APIs for text and image processing, to create a recommendation application, and describe the use of neural networks with Azure Machine Learning

Explore and use HDInsight with Azure Machine Learning

Explore and use R and R Server with Azure Machine Learning, and explain how to deploy and configure SQL Server to support R services

Module 2: Introduction to Azure Machine LearningDescribe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.Lessons

Azure machine learning overview

Introduction to Azure machine learning studio

Developing and hosting Azure machine learning applications

Module 3: Managing DatasetsAt the end of this module the student will be able to upload and explore various types of data in Azure machine learning.Lessons

Categorizing your data

Importing data to Azure machine learning

Exploring and transforming data in Azure machine learning

Module 4: Preparing Data for use with Azure Machine LearningThis module provides techniques to prepare datasets for use with Azure machine learning.Lessons

Data pre-processing

Handling incomplete datasets

Module 5: Using Feature Engineering and SelectionThis module describes how to explore and use feature engineering and selection techniques on datasets that are to be used with Azure machine learning.Lessons

Module 7: Using Classification and Clustering with Azure machine learning modelsThis module describes how to use classification and clustering algorithms with Azure machine learning.Lessons

Using classification algorithms

Clustering techniques

Selecting algorithms

Module 8: Using R and Python with Azure Machine LearningThis module describes how to use R and Python with azure machine learning and choose when to use a particular language.Lessons

Using R

Using Python

Incorporating R and Python into Machine Learning experiments

Module 9: Initializing and Optimizing Machine Learning ModelsThis module describes how to use hyper-parameters and multiple algorithms and models, and be able to score and evaluate models.Lessons

Using hyper-parameters

Using multiple algorithms and models

Scoring and evaluating Models

Module 10: Using Azure Machine Learning ModelsThis module explores how to provide end users with Azure machine learning services, and how to share data generated from Azure machine learning models.Lessons

Deploying and publishing models

Consuming Experiments

Module 11: Using Cognitive ServicesThis module introduces the cognitive services APIs for text and image processing to create a recommendation application, and describes the use of neural networks with Azure machine learning.Lessons

Cognitive services overview

Processing language

Processing images and video

Recommending products

Module 12: Using Machine Learning with HDInsightThis module describes how use HDInsight with Azure machine learning.Lessons

Introduction to HDInsight

HDInsight cluster types

HDInsight and machine learning models

Module 13: Using R Services with Machine LearningThis module describes how to use R and R server with Azure machine learning, and explain how to deploy and configure SQL Server and support R services.Lessons